Overview

Dataset statistics

Number of variables35
Number of observations4424
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 MiB
Average record size in memory280.0 B

Variable types

Categorical12
Numeric17
Boolean6

Alerts

curricular_units_1st_sem_(credited) is highly overall correlated with curricular_units_2nd_sem_(credited)High correlation
curricular_units_1st_sem_(enrolled) is highly overall correlated with curricular_units_1st_sem_(approved) and 2 other fieldsHigh correlation
curricular_units_1st_sem_(evaluations) is highly overall correlated with curricular_units_2nd_sem_(evaluations)High correlation
curricular_units_1st_sem_(approved) is highly overall correlated with curricular_units_1st_sem_(enrolled) and 4 other fieldsHigh correlation
curricular_units_1st_sem_(grade) is highly overall correlated with curricular_units_1st_sem_(approved) and 2 other fieldsHigh correlation
curricular_units_2nd_sem_(credited) is highly overall correlated with curricular_units_1st_sem_(credited)High correlation
curricular_units_2nd_sem_(enrolled) is highly overall correlated with curricular_units_1st_sem_(enrolled) and 2 other fieldsHigh correlation
curricular_units_2nd_sem_(evaluations) is highly overall correlated with curricular_units_1st_sem_(evaluations)High correlation
curricular_units_2nd_sem_(approved) is highly overall correlated with curricular_units_1st_sem_(enrolled) and 5 other fieldsHigh correlation
curricular_units_2nd_sem_(grade) is highly overall correlated with curricular_units_1st_sem_(approved) and 2 other fieldsHigh correlation
application_mode is highly overall correlated with internationalHigh correlation
course is highly overall correlated with daytime/evening_attendanceHigh correlation
daytime/evening_attendance is highly overall correlated with courseHigh correlation
nationality is highly overall correlated with internationalHigh correlation
mother's_occupation is highly overall correlated with father's_occupationHigh correlation
father's_occupation is highly overall correlated with mother's_occupationHigh correlation
international is highly overall correlated with application_mode and 1 other fieldsHigh correlation
target is highly overall correlated with curricular_units_2nd_sem_(approved)High correlation
marital_status is highly imbalanced (75.3%)Imbalance
daytime/evening_attendance is highly imbalanced (50.3%)Imbalance
previous_qualification is highly imbalanced (73.4%)Imbalance
nationality is highly imbalanced (94.3%)Imbalance
educational_special_needs is highly imbalanced (90.9%)Imbalance
international is highly imbalanced (83.2%)Imbalance
curricular_units_1st_sem_(credited) has 3847 (87.0%) zerosZeros
curricular_units_1st_sem_(enrolled) has 180 (4.1%) zerosZeros
curricular_units_1st_sem_(evaluations) has 349 (7.9%) zerosZeros
curricular_units_1st_sem_(approved) has 718 (16.2%) zerosZeros
curricular_units_1st_sem_(grade) has 718 (16.2%) zerosZeros
curricular_units_1st_sem_(without_evaluations) has 4130 (93.4%) zerosZeros
curricular_units_2nd_sem_(credited) has 3894 (88.0%) zerosZeros
curricular_units_2nd_sem_(enrolled) has 180 (4.1%) zerosZeros
curricular_units_2nd_sem_(evaluations) has 401 (9.1%) zerosZeros
curricular_units_2nd_sem_(approved) has 870 (19.7%) zerosZeros
curricular_units_2nd_sem_(grade) has 870 (19.7%) zerosZeros
curricular_units_2nd_sem_(without_evaluations) has 4142 (93.6%) zerosZeros

Reproduction

Analysis started2023-01-11 19:46:21.009225
Analysis finished2023-01-11 19:47:24.691349
Duration1 minute and 3.68 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

marital_status
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Single
3919 
Married
 
379
Divorced
 
91
Facto union
 
25
Legally separated
 
6

Length

Max length17
Median length6
Mean length6.1708861
Min length6

Characters and Unicode

Total characters27300
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSingle
2nd rowSingle
3rd rowSingle
4th rowSingle
5th rowMarried

Common Values

ValueCountFrequency (%)
Single 3919
88.6%
Married 379
 
8.6%
Divorced 91
 
2.1%
Facto union 25
 
0.6%
Legally separated 6
 
0.1%
Widower 4
 
0.1%

Length

2023-01-11T16:47:24.822879image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T16:47:25.186441image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
single 3919
88.0%
married 379
 
8.5%
divorced 91
 
2.0%
facto 25
 
0.6%
union 25
 
0.6%
legally 6
 
0.1%
separated 6
 
0.1%
widower 4
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 4418
16.2%
e 4411
16.2%
n 3969
14.5%
l 3931
14.4%
g 3925
14.4%
S 3919
14.4%
r 859
 
3.1%
d 480
 
1.8%
a 422
 
1.5%
M 379
 
1.4%
Other values (14) 587
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 22845
83.7%
Uppercase Letter 4424
 
16.2%
Space Separator 31
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 4418
19.3%
e 4411
19.3%
n 3969
17.4%
l 3931
17.2%
g 3925
17.2%
r 859
 
3.8%
d 480
 
2.1%
a 422
 
1.8%
o 145
 
0.6%
c 116
 
0.5%
Other values (7) 169
 
0.7%
Uppercase Letter
ValueCountFrequency (%)
S 3919
88.6%
M 379
 
8.6%
D 91
 
2.1%
F 25
 
0.6%
L 6
 
0.1%
W 4
 
0.1%
Space Separator
ValueCountFrequency (%)
31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 27269
99.9%
Common 31
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 4418
16.2%
e 4411
16.2%
n 3969
14.6%
l 3931
14.4%
g 3925
14.4%
S 3919
14.4%
r 859
 
3.2%
d 480
 
1.8%
a 422
 
1.5%
M 379
 
1.4%
Other values (13) 556
 
2.0%
Common
ValueCountFrequency (%)
31
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 27300
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 4418
16.2%
e 4411
16.2%
n 3969
14.5%
l 3931
14.4%
g 3925
14.4%
S 3919
14.4%
r 859
 
3.1%
d 480
 
1.8%
a 422
 
1.5%
M 379
 
1.4%
Other values (14) 587
 
2.2%

application_mode
Categorical

Distinct18
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
1st phase - general contingent
1708 
2nd phase - general contingent
872 
Over 23 years old
785 
Change of course
312 
Technological specialization diploma holders
213 
Other values (13)
534 

Length

Max length51
Median length30
Mean length27.182866
Min length8

Characters and Unicode

Total characters120257
Distinct characters48
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row2nd phase - general contingent
2nd rowInternational student (bachelor)
3rd row1st phase - general contingent
4th row2nd phase - general contingent
5th rowOver 23 years old

Common Values

ValueCountFrequency (%)
1st phase - general contingent 1708
38.6%
2nd phase - general contingent 872
19.7%
Over 23 years old 785
17.7%
Change of course 312
 
7.1%
Technological specialization diploma holders 213
 
4.8%
Holders of other higher courses 139
 
3.1%
3rd phase - general contingent 124
 
2.8%
Transfer 77
 
1.7%
Change of institution/course 59
 
1.3%
1st phase - special contingent (Madeira Island) 38
 
0.9%
Other values (8) 97
 
2.2%

Length

2023-01-11T16:47:25.540516image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2758
13.7%
contingent 2758
13.7%
phase 2758
13.7%
general 2704
13.5%
1st 1762
8.8%
2nd 872
 
4.3%
over 785
 
3.9%
23 785
 
3.9%
years 785
 
3.9%
old 785
 
3.9%
Other values (33) 3310
16.5%

Most occurring characters

ValueCountFrequency (%)
15638
13.0%
e 14799
12.3%
n 13056
10.9%
t 7974
 
6.6%
a 7875
 
6.5%
s 6847
 
5.7%
g 6186
 
5.1%
o 6166
 
5.1%
r 5895
 
4.9%
l 4968
 
4.1%
Other values (38) 30853
25.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 95371
79.3%
Space Separator 15638
 
13.0%
Decimal Number 4405
 
3.7%
Dash Punctuation 2770
 
2.3%
Uppercase Letter 1806
 
1.5%
Other Punctuation 92
 
0.1%
Close Punctuation 88
 
0.1%
Open Punctuation 87
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14799
15.5%
n 13056
13.7%
t 7974
8.4%
a 7875
8.3%
s 6847
 
7.2%
g 6186
 
6.5%
o 6166
 
6.5%
r 5895
 
6.2%
l 4968
 
5.2%
i 4320
 
4.5%
Other values (11) 17285
18.1%
Uppercase Letter
ValueCountFrequency (%)
O 801
44.4%
C 372
20.6%
T 290
 
16.1%
H 139
 
7.7%
I 86
 
4.8%
M 38
 
2.1%
S 35
 
1.9%
A 18
 
1.0%
N 15
 
0.8%
B 10
 
0.6%
Other values (2) 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 1765
40.1%
2 1661
37.7%
3 917
20.8%
9 27
 
0.6%
5 12
 
0.3%
8 10
 
0.2%
4 10
 
0.2%
6 3
 
0.1%
Other Punctuation
ValueCountFrequency (%)
/ 75
81.5%
. 15
 
16.3%
, 2
 
2.2%
Space Separator
ValueCountFrequency (%)
15638
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2770
100.0%
Close Punctuation
ValueCountFrequency (%)
) 88
100.0%
Open Punctuation
ValueCountFrequency (%)
( 87
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 97177
80.8%
Common 23080
 
19.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14799
15.2%
n 13056
13.4%
t 7974
8.2%
a 7875
8.1%
s 6847
 
7.0%
g 6186
 
6.4%
o 6166
 
6.3%
r 5895
 
6.1%
l 4968
 
5.1%
i 4320
 
4.4%
Other values (23) 19091
19.6%
Common
ValueCountFrequency (%)
15638
67.8%
- 2770
 
12.0%
1 1765
 
7.6%
2 1661
 
7.2%
3 917
 
4.0%
) 88
 
0.4%
( 87
 
0.4%
/ 75
 
0.3%
9 27
 
0.1%
. 15
 
0.1%
Other values (5) 37
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120257
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15638
13.0%
e 14799
12.3%
n 13056
10.9%
t 7974
 
6.6%
a 7875
 
6.5%
s 6847
 
5.7%
g 6186
 
5.1%
o 6166
 
5.1%
r 5895
 
4.9%
l 4968
 
4.1%
Other values (38) 30853
25.7%

application_order
Real number (ℝ)

Distinct8
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7278481
Minimum0
Maximum9
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:25.864549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.3137931
Coefficient of variation (CV)0.76036376
Kurtosis2.6512887
Mean1.7278481
Median Absolute Deviation (MAD)0
Skewness1.88105
Sum7644
Variance1.7260523
MonotonicityNot monotonic
2023-01-11T16:47:26.149980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 3026
68.4%
2 547
 
12.4%
3 309
 
7.0%
4 249
 
5.6%
5 154
 
3.5%
6 137
 
3.1%
9 1
 
< 0.1%
0 1
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 3026
68.4%
2 547
 
12.4%
3 309
 
7.0%
4 249
 
5.6%
5 154
 
3.5%
6 137
 
3.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
6 137
 
3.1%
5 154
 
3.5%
4 249
 
5.6%
3 309
 
7.0%
2 547
 
12.4%
1 3026
68.4%
0 1
 
< 0.1%

course
Categorical

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Nursing
766 
Management
380 
Social Service
355 
Veterinary Nursing
337 
Journalism and Communication
331 
Other values (12)
2255 

Length

Max length36
Median length28
Mean length17.993445
Min length7

Characters and Unicode

Total characters79603
Distinct characters38
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAnimation and Multimedia Design
2nd rowTourism
3rd rowCommunication Design
4th rowJournalism and Communication
5th rowSocial Service (evening attendance)

Common Values

ValueCountFrequency (%)
Nursing 766
17.3%
Management 380
 
8.6%
Social Service 355
 
8.0%
Veterinary Nursing 337
 
7.6%
Journalism and Communication 331
 
7.5%
Advertising and Marketing Management 268
 
6.1%
Management (evening attendance) 268
 
6.1%
Tourism 252
 
5.7%
Communication Design 226
 
5.1%
Animation and Multimedia Design 215
 
4.9%
Other values (7) 1026
23.2%

Length

2023-01-11T16:47:26.481982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nursing 1103
 
12.1%
management 916
 
10.1%
and 814
 
8.9%
social 570
 
6.3%
service 570
 
6.3%
communication 557
 
6.1%
evening 483
 
5.3%
attendance 483
 
5.3%
design 441
 
4.8%
veterinary 337
 
3.7%
Other values (17) 2832
31.1%

Most occurring characters

ValueCountFrequency (%)
n 10203
12.8%
i 8163
 
10.3%
e 7459
 
9.4%
a 6745
 
8.5%
4682
 
5.9%
t 4257
 
5.3%
r 4255
 
5.3%
g 4127
 
5.2%
m 3423
 
4.3%
o 3324
 
4.2%
Other values (28) 22965
28.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66629
83.7%
Uppercase Letter 7326
 
9.2%
Space Separator 4682
 
5.9%
Open Punctuation 483
 
0.6%
Close Punctuation 483
 
0.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 10203
15.3%
i 8163
12.3%
e 7459
11.2%
a 6745
10.1%
t 4257
 
6.4%
r 4255
 
6.4%
g 4127
 
6.2%
m 3423
 
5.1%
o 3324
 
5.0%
u 3097
 
4.6%
Other values (10) 11576
17.4%
Uppercase Letter
ValueCountFrequency (%)
M 1399
19.1%
S 1140
15.6%
N 1103
15.1%
A 693
9.5%
C 557
 
7.6%
E 503
 
6.9%
D 441
 
6.0%
V 337
 
4.6%
J 331
 
4.5%
T 264
 
3.6%
Other values (5) 558
 
7.6%
Space Separator
ValueCountFrequency (%)
4682
100.0%
Open Punctuation
ValueCountFrequency (%)
( 483
100.0%
Close Punctuation
ValueCountFrequency (%)
) 483
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 73955
92.9%
Common 5648
 
7.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 10203
13.8%
i 8163
11.0%
e 7459
 
10.1%
a 6745
 
9.1%
t 4257
 
5.8%
r 4255
 
5.8%
g 4127
 
5.6%
m 3423
 
4.6%
o 3324
 
4.5%
u 3097
 
4.2%
Other values (25) 18902
25.6%
Common
ValueCountFrequency (%)
4682
82.9%
( 483
 
8.6%
) 483
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79603
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 10203
12.8%
i 8163
 
10.3%
e 7459
 
9.4%
a 6745
 
8.5%
4682
 
5.9%
t 4257
 
5.3%
r 4255
 
5.3%
g 4127
 
5.2%
m 3423
 
4.3%
o 3324
 
4.2%
Other values (28) 22965
28.8%

daytime/evening_attendance
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
daytime
3941 
evening
483 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters30968
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdaytime
2nd rowdaytime
3rd rowdaytime
4th rowdaytime
5th rowevening

Common Values

ValueCountFrequency (%)
daytime 3941
89.1%
evening 483
 
10.9%

Length

2023-01-11T16:47:26.808916image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T16:47:27.136871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
daytime 3941
89.1%
evening 483
 
10.9%

Most occurring characters

ValueCountFrequency (%)
e 4907
15.8%
i 4424
14.3%
d 3941
12.7%
a 3941
12.7%
y 3941
12.7%
t 3941
12.7%
m 3941
12.7%
n 966
 
3.1%
v 483
 
1.6%
g 483
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 30968
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 4907
15.8%
i 4424
14.3%
d 3941
12.7%
a 3941
12.7%
y 3941
12.7%
t 3941
12.7%
m 3941
12.7%
n 966
 
3.1%
v 483
 
1.6%
g 483
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 30968
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 4907
15.8%
i 4424
14.3%
d 3941
12.7%
a 3941
12.7%
y 3941
12.7%
t 3941
12.7%
m 3941
12.7%
n 966
 
3.1%
v 483
 
1.6%
g 483
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30968
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 4907
15.8%
i 4424
14.3%
d 3941
12.7%
a 3941
12.7%
y 3941
12.7%
t 3941
12.7%
m 3941
12.7%
n 966
 
3.1%
v 483
 
1.6%
g 483
 
1.6%
Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Secondary education
3717 
Technological specialization course
 
219
Basic education 3rd cycle (9th/10th/11th year)
 
162
Higher education - degree
 
126
Other - 11th year of schooling
 
45
Other values (12)
 
155

Length

Max length46
Median length19
Mean length21.643309
Min length19

Characters and Unicode

Total characters95750
Distinct characters42
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowSecondary education
2nd rowSecondary education
3rd rowSecondary education
4th rowSecondary education
5th rowSecondary education

Common Values

ValueCountFrequency (%)
Secondary education 3717
84.0%
Technological specialization course 219
 
5.0%
Basic education 3rd cycle (9th/10th/11th year) 162
 
3.7%
Higher education - degree 126
 
2.8%
Other - 11th year of schooling 45
 
1.0%
Higher education - degree (1st cycle) 40
 
0.9%
Professional higher technical course 36
 
0.8%
Higher education - bachelor's degree 23
 
0.5%
Frequency of higher education 16
 
0.4%
12th year of schooling - not completed 11
 
0.2%
Other values (7) 29
 
0.7%

Length

2023-01-11T16:47:27.565861image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
education 4106
38.6%
secondary 3717
34.9%
266
 
2.5%
higher 256
 
2.4%
course 255
 
2.4%
year 232
 
2.2%
technological 219
 
2.1%
specialization 219
 
2.1%
cycle 215
 
2.0%
degree 189
 
1.8%
Other values (21) 963
 
9.1%

Most occurring characters

ValueCountFrequency (%)
e 10007
10.5%
c 9526
9.9%
o 9071
9.5%
a 8991
9.4%
n 8442
8.8%
d 8205
8.6%
6213
 
6.5%
i 5542
 
5.8%
t 5066
 
5.3%
r 4946
 
5.2%
Other values (32) 19741
20.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 82894
86.6%
Space Separator 6213
 
6.5%
Uppercase Letter 4406
 
4.6%
Decimal Number 1172
 
1.2%
Other Punctuation 369
 
0.4%
Dash Punctuation 266
 
0.3%
Open Punctuation 215
 
0.2%
Close Punctuation 215
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 10007
12.1%
c 9526
11.5%
o 9071
10.9%
a 8991
10.8%
n 8442
10.2%
d 8205
9.9%
i 5542
6.7%
t 5066
6.1%
r 4946
6.0%
u 4377
5.3%
Other values (11) 8721
10.5%
Decimal Number
ValueCountFrequency (%)
1 638
54.4%
0 165
 
14.1%
3 162
 
13.8%
9 162
 
13.8%
2 24
 
2.0%
6 7
 
0.6%
7 7
 
0.6%
8 7
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
S 3717
84.4%
T 219
 
5.0%
H 204
 
4.6%
B 169
 
3.8%
O 45
 
1.0%
P 36
 
0.8%
F 16
 
0.4%
Other Punctuation
ValueCountFrequency (%)
/ 338
91.6%
' 31
 
8.4%
Space Separator
ValueCountFrequency (%)
6213
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 266
100.0%
Open Punctuation
ValueCountFrequency (%)
( 215
100.0%
Close Punctuation
ValueCountFrequency (%)
) 215
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 87300
91.2%
Common 8450
 
8.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 10007
11.5%
c 9526
10.9%
o 9071
10.4%
a 8991
10.3%
n 8442
9.7%
d 8205
9.4%
i 5542
6.3%
t 5066
 
5.8%
r 4946
 
5.7%
u 4377
 
5.0%
Other values (18) 13127
15.0%
Common
ValueCountFrequency (%)
6213
73.5%
1 638
 
7.6%
/ 338
 
4.0%
- 266
 
3.1%
( 215
 
2.5%
) 215
 
2.5%
0 165
 
2.0%
3 162
 
1.9%
9 162
 
1.9%
' 31
 
0.4%
Other values (4) 45
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95750
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 10007
10.5%
c 9526
9.9%
o 9071
9.5%
a 8991
9.4%
n 8442
8.8%
d 8205
8.6%
6213
 
6.5%
i 5542
 
5.8%
t 5066
 
5.3%
r 4946
 
5.2%
Other values (32) 19741
20.6%

nationality
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Portuguese
4314 
Brazilian
 
38
Santomean
 
14
Cape Verdean
 
13
Spanish
 
13
Other values (16)
 
32

Length

Max length21
Median length10
Mean length9.9760398
Min length5

Characters and Unicode

Total characters44134
Distinct characters40
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st rowPortuguese
2nd rowPortuguese
3rd rowPortuguese
4th rowPortuguese
5th rowPortuguese

Common Values

ValueCountFrequency (%)
Portuguese 4314
97.5%
Brazilian 38
 
0.9%
Santomean 14
 
0.3%
Cape Verdean 13
 
0.3%
Spanish 13
 
0.3%
Guinean 5
 
0.1%
Moldova (Republic of) 3
 
0.1%
Italian 3
 
0.1%
Ukrainian 3
 
0.1%
Angolan 2
 
< 0.1%
Other values (11) 16
 
0.4%

Length

2023-01-11T16:47:27.830772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
portuguese 4314
97.1%
brazilian 38
 
0.9%
santomean 14
 
0.3%
cape 13
 
0.3%
verdean 13
 
0.3%
spanish 13
 
0.3%
guinean 5
 
0.1%
moldova 3
 
0.1%
republic 3
 
0.1%
of 3
 
0.1%
Other values (14) 24
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 8693
19.7%
u 8642
19.6%
r 4371
9.9%
o 4345
9.8%
t 4333
9.8%
s 4333
9.8%
g 4317
9.8%
P 4314
9.8%
a 183
 
0.4%
n 132
 
0.3%
Other values (30) 471
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39669
89.9%
Uppercase Letter 4440
 
10.1%
Space Separator 19
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 8693
21.9%
u 8642
21.8%
r 4371
11.0%
o 4345
11.0%
t 4333
10.9%
s 4333
10.9%
g 4317
10.9%
a 183
 
0.5%
n 132
 
0.3%
i 119
 
0.3%
Other values (12) 201
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
P 4314
97.2%
B 38
 
0.9%
S 27
 
0.6%
C 15
 
0.3%
V 13
 
0.3%
G 7
 
0.2%
M 7
 
0.2%
R 7
 
0.2%
U 3
 
0.1%
I 3
 
0.1%
Other values (5) 6
 
0.1%
Space Separator
ValueCountFrequency (%)
19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 44109
99.9%
Common 25
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 8693
19.7%
u 8642
19.6%
r 4371
9.9%
o 4345
9.9%
t 4333
9.8%
s 4333
9.8%
g 4317
9.8%
P 4314
9.8%
a 183
 
0.4%
n 132
 
0.3%
Other values (27) 446
 
1.0%
Common
ValueCountFrequency (%)
19
76.0%
) 3
 
12.0%
( 3
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44134
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 8693
19.7%
u 8642
19.6%
r 4371
9.9%
o 4345
9.8%
t 4333
9.8%
s 4333
9.8%
g 4317
9.8%
P 4314
9.8%
a 183
 
0.4%
n 132
 
0.3%
Other values (30) 471
 
1.1%
Distinct29
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Secondary Education - 12th Year of Schooling or Eq.
1069 
General Course of Administration and Commerce
1009 
General commerce course
953 
Supplementary Accounting and Administration
562 
Higher Education - Degree
438 
Other values (24)
393 

Length

Max length51
Median length47
Mean length38.597649
Min length7

Characters and Unicode

Total characters170756
Distinct characters55
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowGeneral commerce course
2nd rowSecondary Education - 12th Year of Schooling or Eq.
3rd rowGeneral Course of Administration and Commerce
4th rowSupplementary Accounting and Administration
5th rowGeneral Course of Administration and Commerce

Common Values

ValueCountFrequency (%)
Secondary Education - 12th Year of Schooling or Eq. 1069
24.2%
General Course of Administration and Commerce 1009
22.8%
General commerce course 953
21.5%
Supplementary Accounting and Administration 562
12.7%
Higher Education - Degree 438
9.9%
2nd cycle of the general high school course 130
 
2.9%
Higher Education - Bachelor's Degree 83
 
1.9%
Higher Education - Master's 49
 
1.1%
Other - 11th Year of Schooling 42
 
0.9%
Higher Education - Doctorate 21
 
0.5%
Other values (19) 68
 
1.5%

Length

2023-01-11T16:47:28.181137image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
of 2281
 
9.2%
course 2100
 
8.4%
general 2092
 
8.4%
commerce 1962
 
7.9%
1719
 
6.9%
education 1673
 
6.7%
administration 1571
 
6.3%
and 1571
 
6.3%
year 1152
 
4.6%
schooling 1138
 
4.6%
Other values (46) 7607
30.6%

Most occurring characters

ValueCountFrequency (%)
20442
 
12.0%
e 16258
 
9.5%
o 15028
 
8.8%
r 12933
 
7.6%
n 12572
 
7.4%
a 9905
 
5.8%
c 9545
 
5.6%
i 8853
 
5.2%
t 7438
 
4.4%
m 6083
 
3.6%
Other values (45) 51699
30.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 130773
76.6%
Space Separator 20442
 
12.0%
Uppercase Letter 14143
 
8.3%
Decimal Number 2430
 
1.4%
Dash Punctuation 1720
 
1.0%
Other Punctuation 1224
 
0.7%
Open Punctuation 12
 
< 0.1%
Close Punctuation 12
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16258
12.4%
o 15028
11.5%
r 12933
9.9%
n 12572
9.6%
a 9905
 
7.6%
c 9545
 
7.3%
i 8853
 
6.8%
t 7438
 
5.7%
m 6083
 
4.7%
d 6059
 
4.6%
Other values (13) 26099
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 2761
19.5%
E 2738
19.4%
A 2133
15.1%
C 2060
14.6%
G 1962
13.9%
Y 1134
8.0%
H 599
 
4.2%
D 542
 
3.8%
B 92
 
0.7%
M 49
 
0.3%
Other values (5) 73
 
0.5%
Decimal Number
ValueCountFrequency (%)
2 1213
49.9%
1 1175
48.4%
4 10
 
0.4%
7 10
 
0.4%
8 7
 
0.3%
5 4
 
0.2%
6 4
 
0.2%
9 4
 
0.2%
0 2
 
0.1%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 1069
87.3%
' 141
 
11.5%
/ 14
 
1.1%
Space Separator
ValueCountFrequency (%)
20442
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1720
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 144916
84.9%
Common 25840
 
15.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16258
11.2%
o 15028
 
10.4%
r 12933
 
8.9%
n 12572
 
8.7%
a 9905
 
6.8%
c 9545
 
6.6%
i 8853
 
6.1%
t 7438
 
5.1%
m 6083
 
4.2%
d 6059
 
4.2%
Other values (28) 40242
27.8%
Common
ValueCountFrequency (%)
20442
79.1%
- 1720
 
6.7%
2 1213
 
4.7%
1 1175
 
4.5%
. 1069
 
4.1%
' 141
 
0.5%
/ 14
 
0.1%
( 12
 
< 0.1%
) 12
 
< 0.1%
4 10
 
< 0.1%
Other values (7) 32
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 170756
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
20442
 
12.0%
e 16258
 
9.5%
o 15028
 
8.8%
r 12933
 
7.6%
n 12572
 
7.4%
a 9905
 
5.8%
c 9545
 
5.6%
i 8853
 
5.2%
t 7438
 
4.4%
m 6083
 
3.6%
Other values (45) 51699
30.3%
Distinct34
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Basic education 1st cycle (4th/5th year)
1209 
Basic Education 3rd Cycle (9th/10th/11th Year)
968 
Secondary Education - 12th Year of Schooling or Eq.
904 
Basic Education 2nd Cycle (6th/7th/8th Year)
702 
Higher Education - Degree
282 
Other values (29)
359 

Length

Max length51
Median length47
Mean length41.942586
Min length7

Characters and Unicode

Total characters185554
Distinct characters56
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)0.2%

Sample

1st rowOther - 11th Year of Schooling
2nd rowHigher Education - Degree
3rd rowBasic education 1st cycle (4th/5th year)
4th rowBasic education 1st cycle (4th/5th year)
5th rowBasic Education 2nd Cycle (6th/7th/8th Year)

Common Values

ValueCountFrequency (%)
Basic education 1st cycle (4th/5th year) 1209
27.3%
Basic Education 3rd Cycle (9th/10th/11th Year) 968
21.9%
Secondary Education - 12th Year of Schooling or Eq. 904
20.4%
Basic Education 2nd Cycle (6th/7th/8th Year) 702
15.9%
Higher Education - Degree 282
 
6.4%
Unknown 112
 
2.5%
Higher Education - Bachelor's Degree 68
 
1.5%
Higher Education - Master's 39
 
0.9%
Other - 11th Year of Schooling 38
 
0.9%
Technological specialization course 20
 
0.5%
Other values (24) 82
 
1.9%

Length

2023-01-11T16:47:28.506751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
education 4200
15.1%
year 3860
13.9%
cycle 2888
 
10.4%
basic 2879
 
10.3%
1368
 
4.9%
1st 1214
 
4.4%
4th/5th 1209
 
4.3%
of 974
 
3.5%
schooling 970
 
3.5%
3rd 969
 
3.5%
Other values (51) 7309
26.3%

Most occurring characters

ValueCountFrequency (%)
23416
 
12.6%
t 14013
 
7.6%
c 13262
 
7.1%
a 12090
 
6.5%
e 10662
 
5.7%
h 9959
 
5.4%
o 9291
 
5.0%
i 8594
 
4.6%
r 7643
 
4.1%
n 7203
 
3.9%
Other values (46) 69421
37.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 121159
65.3%
Space Separator 23416
 
12.6%
Decimal Number 14186
 
7.6%
Uppercase Letter 14065
 
7.6%
Other Punctuation 5562
 
3.0%
Open Punctuation 2897
 
1.6%
Close Punctuation 2897
 
1.6%
Dash Punctuation 1372
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 14013
11.6%
c 13262
10.9%
a 12090
10.0%
e 10662
8.8%
h 9959
8.2%
o 9291
7.7%
i 8594
 
7.1%
r 7643
 
6.3%
n 7203
 
5.9%
d 6824
 
5.6%
Other values (13) 21618
17.8%
Uppercase Letter
ValueCountFrequency (%)
E 3890
27.7%
B 2947
21.0%
Y 2636
18.7%
S 1865
13.3%
C 1696
12.1%
H 419
 
3.0%
D 369
 
2.6%
U 112
 
0.8%
O 48
 
0.3%
M 41
 
0.3%
Other values (6) 42
 
0.3%
Decimal Number
ValueCountFrequency (%)
1 5111
36.0%
2 1615
 
11.4%
4 1217
 
8.6%
5 1209
 
8.5%
0 972
 
6.9%
9 971
 
6.8%
3 969
 
6.8%
7 714
 
5.0%
8 706
 
5.0%
6 702
 
4.9%
Other Punctuation
ValueCountFrequency (%)
/ 4549
81.8%
. 904
 
16.3%
' 109
 
2.0%
Space Separator
ValueCountFrequency (%)
23416
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2897
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2897
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1372
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 135224
72.9%
Common 50330
 
27.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 14013
 
10.4%
c 13262
 
9.8%
a 12090
 
8.9%
e 10662
 
7.9%
h 9959
 
7.4%
o 9291
 
6.9%
i 8594
 
6.4%
r 7643
 
5.7%
n 7203
 
5.3%
d 6824
 
5.0%
Other values (29) 35683
26.4%
Common
ValueCountFrequency (%)
23416
46.5%
1 5111
 
10.2%
/ 4549
 
9.0%
( 2897
 
5.8%
) 2897
 
5.8%
2 1615
 
3.2%
- 1372
 
2.7%
4 1217
 
2.4%
5 1209
 
2.4%
0 972
 
1.9%
Other values (7) 5075
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 185554
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23416
 
12.6%
t 14013
 
7.6%
c 13262
 
7.1%
a 12090
 
6.5%
e 10662
 
5.7%
h 9959
 
5.4%
o 9291
 
5.0%
i 8594
 
4.6%
r 7643
 
4.1%
n 7203
 
3.9%
Other values (46) 69421
37.4%
Distinct32
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Unskilled Workers
1577 
Administrative staff
817 
Personal Services, Security and Safety Workers and Sellers
530 
Intermediate Level Technicians and Professions
351 
Specialists in Intellectual and Scientific Activities
318 
Other values (27)
831 

Length

Max length106
Median length96
Mean length33.201627
Min length7

Characters and Unicode

Total characters146884
Distinct characters47
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st rowPersonal Services, Security and Safety Workers and Sellers
2nd rowIntermediate Level Technicians and Professions
3rd rowUnskilled Workers
4th rowPersonal Services, Security and Safety Workers and Sellers
5th rowUnskilled Workers

Common Values

ValueCountFrequency (%)
Unskilled Workers 1577
35.6%
Administrative staff 817
18.5%
Personal Services, Security and Safety Workers and Sellers 530
 
12.0%
Intermediate Level Technicians and Professions 351
 
7.9%
Specialists in Intellectual and Scientific Activities 318
 
7.2%
Skilled Workers in Industry, Construction and Craftsmen 272
 
6.1%
Student 144
 
3.3%
Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers 102
 
2.3%
Farmers and Skilled Workers in Agriculture, Fisheries and Forestry 91
 
2.1%
Other Situation 70
 
1.6%
Other values (22) 152
 
3.4%

Length

2023-01-11T16:47:28.783535image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
workers 2523
 
14.2%
and 2501
 
14.1%
unskilled 1577
 
8.9%
administrative 850
 
4.8%
staff 843
 
4.7%
in 692
 
3.9%
personal 546
 
3.1%
services 541
 
3.0%
sellers 534
 
3.0%
security 530
 
3.0%
Other values (79) 6650
37.4%

Most occurring characters

ValueCountFrequency (%)
e 16124
 
11.0%
13363
 
9.1%
s 12024
 
8.2%
i 11979
 
8.2%
r 11279
 
7.7%
n 9990
 
6.8%
t 9120
 
6.2%
a 7828
 
5.3%
l 7550
 
5.1%
d 6197
 
4.2%
Other values (37) 41430
28.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 119087
81.1%
Space Separator 13363
 
9.1%
Uppercase Letter 13242
 
9.0%
Other Punctuation 1153
 
0.8%
Open Punctuation 17
 
< 0.1%
Close Punctuation 17
 
< 0.1%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 16124
13.5%
s 12024
10.1%
i 11979
10.1%
r 11279
9.5%
n 9990
8.4%
t 9120
7.7%
a 7828
 
6.6%
l 7550
 
6.3%
d 6197
 
5.2%
o 5209
 
4.4%
Other values (15) 21787
18.3%
Uppercase Letter
ValueCountFrequency (%)
S 3354
25.3%
W 2506
18.9%
U 1577
11.9%
A 1276
 
9.6%
P 1003
 
7.6%
I 983
 
7.4%
C 544
 
4.1%
L 453
 
3.4%
T 356
 
2.7%
F 287
 
2.2%
Other values (7) 903
 
6.8%
Space Separator
ValueCountFrequency (%)
13363
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1153
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 132329
90.1%
Common 14555
 
9.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 16124
12.2%
s 12024
 
9.1%
i 11979
 
9.1%
r 11279
 
8.5%
n 9990
 
7.5%
t 9120
 
6.9%
a 7828
 
5.9%
l 7550
 
5.7%
d 6197
 
4.7%
o 5209
 
3.9%
Other values (32) 35029
26.5%
Common
ValueCountFrequency (%)
13363
91.8%
, 1153
 
7.9%
( 17
 
0.1%
) 17
 
0.1%
- 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 146884
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 16124
 
11.0%
13363
 
9.1%
s 12024
 
8.2%
i 11979
 
8.2%
r 11279
 
7.7%
n 9990
 
6.8%
t 9120
 
6.2%
a 7828
 
5.3%
l 7550
 
5.1%
d 6197
 
4.2%
Other values (37) 41430
28.2%
Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Unskilled Workers
1010 
Skilled Workers in Industry, Construction and Craftsmen
666 
Personal Services, Security and Safety Workers and Sellers
516 
Administrative staff
386 
Intermediate Level Technicians and Professions
384 
Other values (41)
1462 

Length

Max length106
Median length88
Mean length40.826627
Min length7

Characters and Unicode

Total characters180617
Distinct characters48
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)0.3%

Sample

1st rowUnskilled Workers
2nd rowIntermediate Level Technicians and Professions
3rd rowUnskilled Workers
4th rowIntermediate Level Technicians and Professions
5th rowUnskilled Workers

Common Values

ValueCountFrequency (%)
Unskilled Workers 1010
22.8%
Skilled Workers in Industry, Construction and Craftsmen 666
15.1%
Personal Services, Security and Safety Workers and Sellers 516
11.7%
Administrative staff 386
 
8.7%
Intermediate Level Technicians and Professions 384
 
8.7%
Installation and Machine Operators and Assembly Workers 318
 
7.2%
Armed Forces Professions 266
 
6.0%
Farmers and Skilled Workers in Agriculture, Fisheries and Forestry 242
 
5.5%
Specialists in Intellectual and Scientific Activities 197
 
4.5%
Representatives of the Legislative Power and Executive Bodies, Directors, Directors and Executive Managers 134
 
3.0%
Other values (36) 305
 
6.9%

Length

2023-01-11T16:47:29.146691image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
and 3737
 
16.6%
workers 2795
 
12.4%
in 1136
 
5.0%
unskilled 1031
 
4.6%
skilled 920
 
4.1%
construction 689
 
3.1%
industry 681
 
3.0%
craftsmen 666
 
3.0%
professions 651
 
2.9%
services 522
 
2.3%
Other values (116) 9676
43.0%

Most occurring characters

ValueCountFrequency (%)
e 18776
 
10.4%
18080
 
10.0%
r 15292
 
8.5%
s 14798
 
8.2%
n 13361
 
7.4%
i 12191
 
6.7%
t 10122
 
5.6%
a 9731
 
5.4%
l 8084
 
4.5%
o 8041
 
4.5%
Other values (38) 52141
28.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 144141
79.8%
Space Separator 18080
 
10.0%
Uppercase Letter 16567
 
9.2%
Other Punctuation 1787
 
1.0%
Open Punctuation 20
 
< 0.1%
Close Punctuation 20
 
< 0.1%
Dash Punctuation 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 18776
13.0%
r 15292
10.6%
s 14798
10.3%
n 13361
9.3%
i 12191
8.5%
t 10122
 
7.0%
a 9731
 
6.8%
l 8084
 
5.6%
o 8041
 
5.6%
d 7722
 
5.4%
Other values (15) 26023
18.1%
Uppercase Letter
ValueCountFrequency (%)
S 3578
21.6%
W 2756
16.6%
I 1570
9.5%
A 1418
 
8.6%
C 1332
 
8.0%
P 1304
 
7.9%
U 1031
 
6.2%
F 1007
 
6.1%
L 518
 
3.1%
M 455
 
2.7%
Other values (8) 1598
9.6%
Space Separator
ValueCountFrequency (%)
18080
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1787
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 160708
89.0%
Common 19909
 
11.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 18776
11.7%
r 15292
 
9.5%
s 14798
 
9.2%
n 13361
 
8.3%
i 12191
 
7.6%
t 10122
 
6.3%
a 9731
 
6.1%
l 8084
 
5.0%
o 8041
 
5.0%
d 7722
 
4.8%
Other values (33) 42590
26.5%
Common
ValueCountFrequency (%)
18080
90.8%
, 1787
 
9.0%
( 20
 
0.1%
) 20
 
0.1%
- 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180617
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 18776
 
10.4%
18080
 
10.0%
r 15292
 
8.5%
s 14798
 
8.2%
n 13361
 
7.4%
i 12191
 
6.7%
t 10122
 
5.6%
a 9731
 
5.4%
l 8084
 
4.5%
o 8041
 
4.5%
Other values (38) 52141
28.9%

displaced
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
True
2426 
False
1998 
ValueCountFrequency (%)
True 2426
54.8%
False 1998
45.2%
2023-01-11T16:47:29.501788image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
False
4373 
True
 
51
ValueCountFrequency (%)
False 4373
98.8%
True 51
 
1.2%
2023-01-11T16:47:29.818333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

debtor
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
False
3921 
True
503 
ValueCountFrequency (%)
False 3921
88.6%
True 503
 
11.4%
2023-01-11T16:47:30.127883image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
True
3896 
False
528 
ValueCountFrequency (%)
True 3896
88.1%
False 528
 
11.9%
2023-01-11T16:47:30.444374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
female
2868 
male
1556 

Length

Max length6
Median length6
Mean length5.2965642
Min length4

Characters and Unicode

Total characters23432
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowfemale
5th rowfemale

Common Values

ValueCountFrequency (%)
female 2868
64.8%
male 1556
35.2%

Length

2023-01-11T16:47:30.754980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T16:47:31.110157image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
female 2868
64.8%
male 1556
35.2%

Most occurring characters

ValueCountFrequency (%)
e 7292
31.1%
m 4424
18.9%
a 4424
18.9%
l 4424
18.9%
f 2868
 
12.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 23432
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 7292
31.1%
m 4424
18.9%
a 4424
18.9%
l 4424
18.9%
f 2868
 
12.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 23432
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 7292
31.1%
m 4424
18.9%
a 4424
18.9%
l 4424
18.9%
f 2868
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23432
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 7292
31.1%
m 4424
18.9%
a 4424
18.9%
l 4424
18.9%
f 2868
 
12.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
False
3325 
True
1099 
ValueCountFrequency (%)
False 3325
75.2%
True 1099
 
24.8%
2023-01-11T16:47:31.425934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

age_at_enrollment
Real number (ℝ)

Distinct46
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.265145
Minimum17
Maximum70
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:31.746593image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q119
median20
Q325
95-th percentile41
Maximum70
Range53
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.5878156
Coefficient of variation (CV)0.32614522
Kurtosis4.1268918
Mean23.265145
Median Absolute Deviation (MAD)2
Skewness2.0549884
Sum102925
Variance57.574946
MonotonicityNot monotonic
2023-01-11T16:47:32.118896image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
18 1036
23.4%
19 911
20.6%
20 599
13.5%
21 322
 
7.3%
22 174
 
3.9%
24 131
 
3.0%
23 108
 
2.4%
26 94
 
2.1%
25 93
 
2.1%
27 91
 
2.1%
Other values (36) 865
19.6%
ValueCountFrequency (%)
17 5
 
0.1%
18 1036
23.4%
19 911
20.6%
20 599
13.5%
21 322
 
7.3%
22 174
 
3.9%
23 108
 
2.4%
24 131
 
3.0%
25 93
 
2.1%
26 94
 
2.1%
ValueCountFrequency (%)
70 1
 
< 0.1%
62 1
 
< 0.1%
61 1
 
< 0.1%
60 2
 
< 0.1%
59 3
0.1%
58 3
0.1%
57 2
 
< 0.1%
55 5
0.1%
54 7
0.2%
53 7
0.2%

international
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.4 KiB
False
4314 
True
 
110
ValueCountFrequency (%)
False 4314
97.5%
True 110
 
2.5%
2023-01-11T16:47:32.472648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

curricular_units_1st_sem_(credited)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.70999096
Minimum0
Maximum20
Zeros3847
Zeros (%)87.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:32.759192image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.3605066
Coefficient of variation (CV)3.3246995
Kurtosis19.205727
Mean0.70999096
Median Absolute Deviation (MAD)0
Skewness4.1690488
Sum3141
Variance5.5719915
MonotonicityNot monotonic
2023-01-11T16:47:33.079731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 3847
87.0%
2 94
 
2.1%
1 85
 
1.9%
3 69
 
1.6%
6 51
 
1.2%
4 47
 
1.1%
7 41
 
0.9%
5 41
 
0.9%
8 31
 
0.7%
9 27
 
0.6%
Other values (11) 91
 
2.1%
ValueCountFrequency (%)
0 3847
87.0%
1 85
 
1.9%
2 94
 
2.1%
3 69
 
1.6%
4 47
 
1.1%
5 41
 
0.9%
6 51
 
1.2%
7 41
 
0.9%
8 31
 
0.7%
9 27
 
0.6%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 2
 
< 0.1%
18 4
 
0.1%
17 3
 
0.1%
16 3
 
0.1%
15 5
 
0.1%
14 15
0.3%
13 13
0.3%
12 12
0.3%
11 17
0.4%

curricular_units_1st_sem_(enrolled)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2705696
Minimum0
Maximum26
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:33.398630image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q15
median6
Q37
95-th percentile11
Maximum26
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4801782
Coefficient of variation (CV)0.39552677
Kurtosis8.9379154
Mean6.2705696
Median Absolute Deviation (MAD)1
Skewness1.6190409
Sum27741
Variance6.1512838
MonotonicityNot monotonic
2023-01-11T16:47:33.731219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 1910
43.2%
5 1010
22.8%
7 656
 
14.8%
8 296
 
6.7%
0 180
 
4.1%
12 66
 
1.5%
10 52
 
1.2%
11 45
 
1.0%
9 36
 
0.8%
15 25
 
0.6%
Other values (13) 148
 
3.3%
ValueCountFrequency (%)
0 180
 
4.1%
1 7
 
0.2%
2 9
 
0.2%
3 10
 
0.2%
4 21
 
0.5%
5 1010
22.8%
6 1910
43.2%
7 656
 
14.8%
8 296
 
6.7%
9 36
 
0.8%
ValueCountFrequency (%)
26 1
 
< 0.1%
23 2
 
< 0.1%
21 6
 
0.1%
19 2
 
< 0.1%
18 19
0.4%
17 16
0.4%
16 13
0.3%
15 25
0.6%
14 22
0.5%
13 20
0.5%

curricular_units_1st_sem_(evaluations)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct35
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.2990506
Minimum0
Maximum45
Zeros349
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:34.064005image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum45
Range45
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1791056
Coefficient of variation (CV)0.50356429
Kurtosis5.4630252
Mean8.2990506
Median Absolute Deviation (MAD)2
Skewness0.9766367
Sum36715
Variance17.464923
MonotonicityNot monotonic
2023-01-11T16:47:34.403207image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
8 791
17.9%
7 703
15.9%
6 598
13.5%
9 402
9.1%
0 349
7.9%
10 340
7.7%
11 239
 
5.4%
12 223
 
5.0%
5 220
 
5.0%
13 140
 
3.2%
Other values (25) 419
9.5%
ValueCountFrequency (%)
0 349
7.9%
1 6
 
0.1%
2 8
 
0.2%
3 6
 
0.1%
4 19
 
0.4%
5 220
 
5.0%
6 598
13.5%
7 703
15.9%
8 791
17.9%
9 402
9.1%
ValueCountFrequency (%)
45 2
< 0.1%
36 1
 
< 0.1%
33 1
 
< 0.1%
32 1
 
< 0.1%
31 1
 
< 0.1%
29 2
< 0.1%
28 1
 
< 0.1%
27 2
< 0.1%
26 4
0.1%
25 3
0.1%

curricular_units_1st_sem_(approved)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7066004
Minimum0
Maximum26
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:34.707509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median5
Q36
95-th percentile9
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation3.094238
Coefficient of variation (CV)0.65742526
Kurtosis3.0966799
Mean4.7066004
Median Absolute Deviation (MAD)1
Skewness0.7662624
Sum20822
Variance9.5743087
MonotonicityNot monotonic
2023-01-11T16:47:35.055162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 1171
26.5%
5 723
16.3%
0 718
16.2%
7 471
10.6%
4 433
 
9.8%
3 269
 
6.1%
2 160
 
3.6%
1 127
 
2.9%
8 108
 
2.4%
11 49
 
1.1%
Other values (13) 195
 
4.4%
ValueCountFrequency (%)
0 718
16.2%
1 127
 
2.9%
2 160
 
3.6%
3 269
 
6.1%
4 433
 
9.8%
5 723
16.3%
6 1171
26.5%
7 471
10.6%
8 108
 
2.4%
9 40
 
0.9%
ValueCountFrequency (%)
26 1
 
< 0.1%
21 4
 
0.1%
20 3
 
0.1%
19 2
 
< 0.1%
18 15
0.3%
17 10
 
0.2%
16 5
 
0.1%
15 7
 
0.2%
14 14
0.3%
13 26
0.6%

curricular_units_1st_sem_(grade)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct805
Distinct (%)18.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.640822
Minimum0
Maximum18.875
Zeros718
Zeros (%)16.2%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:35.392905image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111
median12.285714
Q313.4
95-th percentile14.857143
Maximum18.875
Range18.875
Interquartile range (IQR)2.4

Descriptive statistics

Standard deviation4.8436634
Coefficient of variation (CV)0.45519637
Kurtosis0.90846103
Mean10.640822
Median Absolute Deviation (MAD)1.1571429
Skewness-1.5681456
Sum47074.995
Variance23.461075
MonotonicityNot monotonic
2023-01-11T16:47:35.889260image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 718
 
16.2%
12 205
 
4.6%
13 147
 
3.3%
11 138
 
3.1%
11.5 89
 
2.0%
14 85
 
1.9%
12.5 84
 
1.9%
10 82
 
1.9%
12.66666667 82
 
1.9%
12.33333333 82
 
1.9%
Other values (795) 2712
61.3%
ValueCountFrequency (%)
0 718
16.2%
9.8 1
 
< 0.1%
10 82
 
1.9%
10.16666667 1
 
< 0.1%
10.2 8
 
0.2%
10.21428571 1
 
< 0.1%
10.25 7
 
0.2%
10.28571429 1
 
< 0.1%
10.33333333 16
 
0.4%
10.36842105 1
 
< 0.1%
ValueCountFrequency (%)
18.875 1
 
< 0.1%
18 2
 
< 0.1%
17.33333333 2
 
< 0.1%
17.125 1
 
< 0.1%
17.11111111 1
 
< 0.1%
17.00555556 1
 
< 0.1%
17 5
0.1%
16.9 1
 
< 0.1%
16.88571429 1
 
< 0.1%
16.85714286 1
 
< 0.1%
Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13765823
Minimum0
Maximum12
Zeros4130
Zeros (%)93.4%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:36.159386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69088018
Coefficient of variation (CV)5.0188078
Kurtosis89.863208
Mean0.13765823
Median Absolute Deviation (MAD)0
Skewness8.2074031
Sum609
Variance0.47731543
MonotonicityNot monotonic
2023-01-11T16:47:36.471871image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0 4130
93.4%
1 153
 
3.5%
2 79
 
1.8%
3 23
 
0.5%
4 15
 
0.3%
6 6
 
0.1%
7 6
 
0.1%
5 5
 
0.1%
8 4
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
0 4130
93.4%
1 153
 
3.5%
2 79
 
1.8%
3 23
 
0.5%
4 15
 
0.3%
5 5
 
0.1%
6 6
 
0.1%
7 6
 
0.1%
8 4
 
0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
10 1
 
< 0.1%
8 4
 
0.1%
7 6
 
0.1%
6 6
 
0.1%
5 5
 
0.1%
4 15
 
0.3%
3 23
 
0.5%
2 79
1.8%
1 153
3.5%

curricular_units_2nd_sem_(credited)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54181736
Minimum0
Maximum19
Zeros3894
Zeros (%)88.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:36.772821image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4
Maximum19
Range19
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.9185461
Coefficient of variation (CV)3.5409462
Kurtosis24.427266
Mean0.54181736
Median Absolute Deviation (MAD)0
Skewness4.6348195
Sum2397
Variance3.6808193
MonotonicityNot monotonic
2023-01-11T16:47:37.071860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 3894
88.0%
1 107
 
2.4%
2 92
 
2.1%
4 78
 
1.8%
5 68
 
1.5%
3 49
 
1.1%
6 26
 
0.6%
11 20
 
0.5%
7 16
 
0.4%
9 15
 
0.3%
Other values (9) 59
 
1.3%
ValueCountFrequency (%)
0 3894
88.0%
1 107
 
2.4%
2 92
 
2.1%
3 49
 
1.1%
4 78
 
1.8%
5 68
 
1.5%
6 26
 
0.6%
7 16
 
0.4%
8 12
 
0.3%
9 15
 
0.3%
ValueCountFrequency (%)
19 1
 
< 0.1%
18 2
 
< 0.1%
16 2
 
< 0.1%
15 2
 
< 0.1%
14 4
 
0.1%
13 9
0.2%
12 14
0.3%
11 20
0.5%
10 13
0.3%
9 15
0.3%

curricular_units_2nd_sem_(enrolled)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2321429
Minimum0
Maximum23
Zeros180
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:37.375628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median6
Q37
95-th percentile10
Maximum23
Range23
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1959508
Coefficient of variation (CV)0.35235886
Kurtosis7.13474
Mean6.2321429
Median Absolute Deviation (MAD)1
Skewness0.7881135
Sum27571
Variance4.8221997
MonotonicityNot monotonic
2023-01-11T16:47:37.688366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6 1913
43.2%
5 1054
23.8%
8 661
 
14.9%
7 304
 
6.9%
0 180
 
4.1%
11 60
 
1.4%
9 50
 
1.1%
10 48
 
1.1%
12 44
 
1.0%
13 37
 
0.8%
Other values (12) 73
 
1.7%
ValueCountFrequency (%)
0 180
 
4.1%
1 3
 
0.1%
2 5
 
0.1%
3 3
 
0.1%
4 17
 
0.4%
5 1054
23.8%
6 1913
43.2%
7 304
 
6.9%
8 661
 
14.9%
9 50
 
1.1%
ValueCountFrequency (%)
23 2
 
< 0.1%
21 1
 
< 0.1%
19 3
 
0.1%
18 2
 
< 0.1%
17 12
 
0.3%
16 1
 
< 0.1%
15 2
 
< 0.1%
14 22
0.5%
13 37
0.8%
12 44
1.0%

curricular_units_2nd_sem_(evaluations)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0632911
Minimum0
Maximum33
Zeros401
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:37.998222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median8
Q310
95-th percentile15
Maximum33
Range33
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.9479509
Coefficient of variation (CV)0.48962029
Kurtosis2.0682859
Mean8.0632911
Median Absolute Deviation (MAD)2
Skewness0.33649718
Sum35672
Variance15.586317
MonotonicityNot monotonic
2023-01-11T16:47:38.314903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
8 792
17.9%
6 614
13.9%
7 563
12.7%
9 456
10.3%
0 401
9.1%
10 355
8.0%
5 288
 
6.5%
11 255
 
5.8%
12 226
 
5.1%
13 126
 
2.8%
Other values (20) 348
7.9%
ValueCountFrequency (%)
0 401
9.1%
1 3
 
0.1%
2 4
 
0.1%
3 2
 
< 0.1%
4 10
 
0.2%
5 288
 
6.5%
6 614
13.9%
7 563
12.7%
8 792
17.9%
9 456
10.3%
ValueCountFrequency (%)
33 1
 
< 0.1%
28 1
 
< 0.1%
27 2
 
< 0.1%
26 3
 
0.1%
25 1
 
< 0.1%
24 3
 
0.1%
23 4
 
0.1%
22 10
0.2%
21 10
0.2%
20 8
0.2%

curricular_units_2nd_sem_(approved)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4358047
Minimum0
Maximum20
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:38.601603image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q36
95-th percentile8
Maximum20
Range20
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0147639
Coefficient of variation (CV)0.67964306
Kurtosis0.84504466
Mean4.4358047
Median Absolute Deviation (MAD)2
Skewness0.30627938
Sum19624
Variance9.0888014
MonotonicityNot monotonic
2023-01-11T16:47:38.737568image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6 965
21.8%
0 870
19.7%
5 726
16.4%
4 414
9.4%
7 331
 
7.5%
8 321
 
7.3%
3 285
 
6.4%
2 198
 
4.5%
1 114
 
2.6%
11 48
 
1.1%
Other values (10) 152
 
3.4%
ValueCountFrequency (%)
0 870
19.7%
1 114
 
2.6%
2 198
 
4.5%
3 285
 
6.4%
4 414
9.4%
5 726
16.4%
6 965
21.8%
7 331
 
7.5%
8 321
 
7.3%
9 36
 
0.8%
ValueCountFrequency (%)
20 2
 
< 0.1%
19 3
 
0.1%
18 2
 
< 0.1%
17 8
 
0.2%
16 2
 
< 0.1%
14 6
 
0.1%
13 21
0.5%
12 34
0.8%
11 48
1.1%
10 38
0.9%

curricular_units_2nd_sem_(grade)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct786
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.230206
Minimum0
Maximum18.571429
Zeros870
Zeros (%)19.7%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:38.905809image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.75
median12.2
Q313.333333
95-th percentile14.980262
Maximum18.571429
Range18.571429
Interquartile range (IQR)2.5833333

Descriptive statistics

Standard deviation5.210808
Coefficient of variation (CV)0.50935515
Kurtosis0.066567351
Mean10.230206
Median Absolute Deviation (MAD)1.2
Skewness-1.3136502
Sum45258.43
Variance27.15252
MonotonicityNot monotonic
2023-01-11T16:47:39.112171image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 870
 
19.7%
12 170
 
3.8%
11 165
 
3.7%
13 150
 
3.4%
11.5 86
 
1.9%
12.5 84
 
1.9%
14 77
 
1.7%
10 77
 
1.7%
13.5 65
 
1.5%
12.66666667 61
 
1.4%
Other values (776) 2619
59.2%
ValueCountFrequency (%)
0 870
19.7%
10 77
 
1.7%
10.16666667 4
 
0.1%
10.2 4
 
0.1%
10.25 10
 
0.2%
10.33333333 19
 
0.4%
10.375 1
 
< 0.1%
10.4 8
 
0.2%
10.42857143 2
 
< 0.1%
10.44444444 2
 
< 0.1%
ValueCountFrequency (%)
18.57142857 1
< 0.1%
17.71428571 1
< 0.1%
17.69230769 1
< 0.1%
17.6 2
< 0.1%
17.5875 1
< 0.1%
17.42857143 1
< 0.1%
17.16666667 1
< 0.1%
17 2
< 0.1%
16.90909091 1
< 0.1%
16.8 2
< 0.1%
Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.15031646
Minimum0
Maximum12
Zeros4142
Zeros (%)93.6%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:39.371517image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum12
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.75377407
Coefficient of variation (CV)5.0145812
Kurtosis66.811692
Mean0.15031646
Median Absolute Deviation (MAD)0
Skewness7.2677009
Sum665
Variance0.56817535
MonotonicityNot monotonic
2023-01-11T16:47:39.493230image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 4142
93.6%
1 140
 
3.2%
2 48
 
1.1%
3 35
 
0.8%
4 21
 
0.5%
5 17
 
0.4%
6 8
 
0.2%
8 6
 
0.1%
7 5
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
0 4142
93.6%
1 140
 
3.2%
2 48
 
1.1%
3 35
 
0.8%
4 21
 
0.5%
5 17
 
0.4%
6 8
 
0.2%
7 5
 
0.1%
8 6
 
0.1%
12 2
 
< 0.1%
ValueCountFrequency (%)
12 2
 
< 0.1%
8 6
 
0.1%
7 5
 
0.1%
6 8
 
0.2%
5 17
 
0.4%
4 21
 
0.5%
3 35
 
0.8%
2 48
 
1.1%
1 140
 
3.2%
0 4142
93.6%

unemployment_rate
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.566139
Minimum7.6
Maximum16.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.7 KiB
2023-01-11T16:47:39.627002image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7.6
5-th percentile7.6
Q19.4
median11.1
Q313.9
95-th percentile16.2
Maximum16.2
Range8.6
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation2.6638505
Coefficient of variation (CV)0.23031458
Kurtosis-0.99552591
Mean11.566139
Median Absolute Deviation (MAD)1.7
Skewness0.21205105
Sum51168.6
Variance7.0960994
MonotonicityNot monotonic
2023-01-11T16:47:39.747450image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
7.6 571
12.9%
9.4 533
12.0%
10.8 525
11.9%
12.4 445
10.1%
12.7 419
9.5%
11.1 414
9.4%
15.5 397
9.0%
13.9 390
8.8%
8.9 368
8.3%
16.2 362
8.2%
ValueCountFrequency (%)
7.6 571
12.9%
8.9 368
8.3%
9.4 533
12.0%
10.8 525
11.9%
11.1 414
9.4%
12.4 445
10.1%
12.7 419
9.5%
13.9 390
8.8%
15.5 397
9.0%
16.2 362
8.2%
ValueCountFrequency (%)
16.2 362
8.2%
15.5 397
9.0%
13.9 390
8.8%
12.7 419
9.5%
12.4 445
10.1%
11.1 414
9.4%
10.8 525
11.9%
9.4 533
12.0%
8.9 368
8.3%
7.6 571
12.9%

inflation_rate
Real number (ℝ)

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2280289
Minimum-0.8
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative923
Negative (%)20.9%
Memory size34.7 KiB
2023-01-11T16:47:39.868069image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-0.8
5-th percentile-0.8
Q10.3
median1.4
Q32.6
95-th percentile3.7
Maximum3.7
Range4.5
Interquartile range (IQR)2.3

Descriptive statistics

Standard deviation1.3827107
Coefficient of variation (CV)1.1259594
Kurtosis-1.0390334
Mean1.2280289
Median Absolute Deviation (MAD)1.2
Skewness0.25237535
Sum5432.8
Variance1.9118889
MonotonicityNot monotonic
2023-01-11T16:47:39.987262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1.4 893
20.2%
2.6 571
12.9%
-0.8 533
12.0%
0.5 445
10.1%
3.7 419
9.5%
0.6 414
9.4%
2.8 397
9.0%
-0.3 390
8.8%
0.3 362
8.2%
ValueCountFrequency (%)
-0.8 533
12.0%
-0.3 390
8.8%
0.3 362
8.2%
0.5 445
10.1%
0.6 414
9.4%
1.4 893
20.2%
2.6 571
12.9%
2.8 397
9.0%
3.7 419
9.5%
ValueCountFrequency (%)
3.7 419
9.5%
2.8 397
9.0%
2.6 571
12.9%
1.4 893
20.2%
0.6 414
9.4%
0.5 445
10.1%
0.3 362
8.2%
-0.3 390
8.8%
-0.8 533
12.0%

gdp
Real number (ℝ)

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0019688065
Minimum-4.06
Maximum3.51
Zeros0
Zeros (%)0.0%
Negative1711
Negative (%)38.7%
Memory size34.7 KiB
2023-01-11T16:47:40.119696image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum-4.06
5-th percentile-4.06
Q1-1.7
median0.32
Q31.79
95-th percentile3.51
Maximum3.51
Range7.57
Interquartile range (IQR)3.49

Descriptive statistics

Standard deviation2.2699354
Coefficient of variation (CV)1152.95
Kurtosis-1.0016532
Mean0.0019688065
Median Absolute Deviation (MAD)1.47
Skewness-0.39406821
Sum8.71
Variance5.1526069
MonotonicityNot monotonic
2023-01-11T16:47:40.253779image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.32 571
12.9%
-3.12 533
12.0%
1.74 525
11.9%
1.79 445
10.1%
-1.7 419
9.5%
2.02 414
9.4%
-4.06 397
9.0%
0.79 390
8.8%
3.51 368
8.3%
-0.92 362
8.2%
ValueCountFrequency (%)
-4.06 397
9.0%
-3.12 533
12.0%
-1.7 419
9.5%
-0.92 362
8.2%
0.32 571
12.9%
0.79 390
8.8%
1.74 525
11.9%
1.79 445
10.1%
2.02 414
9.4%
3.51 368
8.3%
ValueCountFrequency (%)
3.51 368
8.3%
2.02 414
9.4%
1.79 445
10.1%
1.74 525
11.9%
0.79 390
8.8%
0.32 571
12.9%
-0.92 362
8.2%
-1.7 419
9.5%
-3.12 533
12.0%
-4.06 397
9.0%

target
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.7 KiB
Graduate
2209 
Dropout
1421 
Enrolled
794 

Length

Max length8
Median length8
Mean length7.6787975
Min length7

Characters and Unicode

Total characters33971
Distinct characters13
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDropout
2nd rowGraduate
3rd rowDropout
4th rowGraduate
5th rowGraduate

Common Values

ValueCountFrequency (%)
Graduate 2209
49.9%
Dropout 1421
32.1%
Enrolled 794
 
17.9%

Length

2023-01-11T16:47:40.427438image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-11T16:47:40.598829image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
graduate 2209
49.9%
dropout 1421
32.1%
enrolled 794
 
17.9%

Most occurring characters

ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 29547
87.0%
Uppercase Letter 4424
 
13.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 4424
15.0%
a 4418
15.0%
o 3636
12.3%
u 3630
12.3%
t 3630
12.3%
d 3003
10.2%
e 3003
10.2%
l 1588
 
5.4%
p 1421
 
4.8%
n 794
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
G 2209
49.9%
D 1421
32.1%
E 794
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 33971
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33971
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 4424
13.0%
a 4418
13.0%
o 3636
10.7%
u 3630
10.7%
t 3630
10.7%
d 3003
8.8%
e 3003
8.8%
G 2209
6.5%
l 1588
 
4.7%
D 1421
 
4.2%
Other values (3) 3009
8.9%

Interactions

2023-01-11T16:47:19.748116image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:27.305740image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:30.343386image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:32.992625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:36.398205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:39.057103image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:41.774070image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:44.732453image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:47.996642image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:50.797245image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:53.538835image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:56.474289image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:59.592697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:02.620321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:07.117418image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:11.097058image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:15.758063image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:19.896026image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:27.567846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:30.489348image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:33.149503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:36.540574image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:39.205913image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:41.920496image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:45.000083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:48.155451image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:50.943705image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:53.674887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:56.735930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:59.742873image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:02.777689image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:07.254599image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:11.382751image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:15.946800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:20.045963image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:27.847634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:30.637578image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:33.311581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:36.695629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:39.349776image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:42.070059image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:45.154272image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:48.312509image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:51.107083image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:53.821392image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:57.011205image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:59.905699image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:02.934579image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:07.400601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:11.663688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:16.215027image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:20.229697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:28.124993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:30.809154image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:33.492101image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:36.861317image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:39.522228image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:42.238439image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:45.332060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:48.592993image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:51.276321image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:54.010287image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:57.302328image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:00.112236image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:03.173504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:07.562296image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:11.993008image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:16.392365image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:20.500983image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:28.302443image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:30.956406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:33.650507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:37.017428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:39.668170image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:42.391330image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:45.494082image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:48.747194image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:51.434722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:54.171145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:57.537804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:00.269123image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:03.450647image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:07.710949image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:12.355583image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:16.664224image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:20.649504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:28.440990image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:31.106210image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:33.810876image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:37.160507image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:39.812546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:42.535277image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:45.647930image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:48.896215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:51.591555image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:54.314701image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:57.687845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:00.427282image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:03.723014image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:07.980870image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:12.631133image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:16.914355image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:20.909980image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:28.659428image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:31.256298image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:33.967187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:37.319644image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:39.956467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:42.688549image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:45.814804image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:49.047524image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:51.749852image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:54.456415image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:57.840707image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:00.597494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:03.997763image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:08.219834image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:12.896512image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:17.168937image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:21.205087image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:28.822019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:31.422445image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:34.255978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:37.485626image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:40.123791image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:42.849557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:45.994271image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:49.214478image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:51.918950image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:54.740724image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:58.008366image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:00.766670image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:04.301121image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:08.383944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:13.171394image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-11T16:46:52.083648image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:54.889996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
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2023-01-11T16:46:58.639300image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:01.445934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:05.502704image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:09.234164image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:14.248756image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:18.489335image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:22.358550image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:29.596663image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:32.225754image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:35.564736image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:38.284878image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:40.909681image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:43.640891image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:47.197897image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:50.018241image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:52.739581image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:55.513268image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:58.814036image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:01.739805image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:05.829413image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:09.520807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:14.530978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:18.764566image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:22.546161image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:29.753388image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:32.388015image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:35.740925image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:38.446494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:41.066815image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:43.806262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:47.358982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:50.183996image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:52.909435image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:55.668374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:58.979085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:01.914783image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:06.154722image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:09.803189image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:14.810868image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:19.042333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:22.709855image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:29.907125image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:32.544372image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:35.912739image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:38.608480image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:41.223887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:43.958054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:47.527807image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:50.339109image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:53.069850image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:55.824127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:59.136162image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:02.097406image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:06.455848image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:10.108067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:15.077845image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:19.316023image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:22.851914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:30.044982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:32.689045image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:36.065693image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:38.748801image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:41.483363image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:44.201477image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:47.676395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:50.489461image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:53.222504image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:55.958813image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:59.284092image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:02.248234image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:06.596001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:10.362399image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:15.338356image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:19.455420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:23.000575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:30.184424image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:32.831994image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:36.224692image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:38.896637image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:41.620288image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:44.454902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:47.826215image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:50.633028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:53.367938image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:56.202307image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:46:59.426614image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:02.402145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:06.802761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:10.813772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:15.541032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2023-01-11T16:47:19.599802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2023-01-11T16:47:40.808800image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
application_orderage_at_enrollmentcurricular_units_1st_sem_(credited)curricular_units_1st_sem_(enrolled)curricular_units_1st_sem_(evaluations)curricular_units_1st_sem_(approved)curricular_units_1st_sem_(grade)curricular_units_1st_sem_(without_evaluations)curricular_units_2nd_sem_(credited)curricular_units_2nd_sem_(enrolled)curricular_units_2nd_sem_(evaluations)curricular_units_2nd_sem_(approved)curricular_units_2nd_sem_(grade)curricular_units_2nd_sem_(without_evaluations)unemployment_rateinflation_rategdpmarital_statusapplication_modecoursedaytime/evening_attendanceprevious_qualificationnationalitymother's_qualificationfather's_qualificationmother's_occupationfather's_occupationdisplacededucational_special_needsdebtortuition_fees_up_to_dategenderscholarship_holderinternationaltarget
application_order1.000-0.366-0.2020.056-0.0960.0900.074-0.041-0.2010.075-0.0530.1060.065-0.030-0.104-0.0110.0300.0700.1610.1430.1850.0860.0000.0330.0380.0000.0000.3820.0000.0900.0730.1080.0890.0000.079
age_at_enrollment-0.3661.0000.2990.0010.171-0.166-0.2100.0610.301-0.0310.083-0.188-0.2130.0940.0190.018-0.0570.3130.2880.1890.4890.1950.0000.1490.1330.0840.0620.3890.0000.1330.2090.1620.2120.0000.219
curricular_units_1st_sem_(credited)-0.2020.2991.0000.4230.3660.3620.1010.1000.9140.3760.3310.2890.0930.0640.024-0.0020.0220.0500.2670.1170.1710.1830.0000.0870.0710.0380.0230.1070.0000.0570.0000.0270.0860.0000.045
curricular_units_1st_sem_(enrolled)0.0560.0010.4231.0000.4200.7070.363-0.0190.4400.9620.4310.6530.351-0.0210.1080.0170.0180.0670.1980.4340.2190.1360.0000.0820.0490.0340.0030.1500.0430.0470.0830.2130.1560.0000.173
curricular_units_1st_sem_(evaluations)-0.0960.1710.3660.4201.0000.2620.1120.2020.3690.3860.6940.2520.0900.1530.067-0.040-0.0970.0320.1430.2410.0630.1030.1690.0320.0770.0000.0000.0980.0000.0610.1030.0790.1870.0560.258
curricular_units_1st_sem_(approved)0.090-0.1660.3620.7070.2621.0000.640-0.0690.3670.6990.3640.8920.663-0.0730.074-0.0020.0590.0510.1870.2360.1730.1280.0000.0980.0420.0560.0000.1290.0000.1450.2800.2370.2640.0000.455
curricular_units_1st_sem_(grade)0.074-0.2100.1010.3630.1120.6401.000-0.0170.0970.3680.1820.6290.762-0.0470.045-0.0370.0920.0390.1110.2710.1380.0930.0260.0650.0520.0570.0530.0870.0000.1060.2480.1930.1810.0000.383
curricular_units_1st_sem_(without_evaluations)-0.0410.0610.100-0.0190.202-0.069-0.0171.0000.059-0.0320.096-0.055-0.0410.384-0.067-0.068-0.1840.0550.0580.1010.0190.0760.1680.0650.0970.0520.0000.0000.0000.0310.0780.0000.0570.0570.062
curricular_units_2nd_sem_(credited)-0.2010.3010.9140.4400.3690.3670.0970.0591.0000.4140.3420.3190.1030.0820.012-0.0010.0240.0470.2320.1160.1820.1760.0000.1210.0340.0270.0000.1280.0000.0540.0000.0280.0750.0000.042
curricular_units_2nd_sem_(enrolled)0.075-0.0310.3760.9620.3860.6990.368-0.0320.4141.0000.4400.6740.364-0.0270.1390.0090.0190.0340.2000.4370.1870.1080.0000.1220.0370.0370.0570.1380.0000.0750.1240.1620.1130.0300.139
curricular_units_2nd_sem_(evaluations)-0.0530.0830.3310.4310.6940.3640.1820.0960.3420.4401.0000.3030.1720.1590.061-0.024-0.0040.0150.1280.2450.1080.1140.0800.0040.0000.0000.0000.0680.0290.0590.1240.1090.1660.0110.274
curricular_units_2nd_sem_(approved)0.106-0.1880.2890.6530.2520.8920.629-0.0550.3190.6740.3031.0000.694-0.0640.070-0.0150.0480.0480.2890.2880.1010.1180.0000.0920.0350.0580.0240.1160.0000.1800.3130.2620.2730.0250.516
curricular_units_2nd_sem_(grade)0.065-0.2130.0930.3510.0900.6630.762-0.0410.1030.3640.1720.6941.000-0.0520.042-0.0430.1050.0330.1100.2280.0850.1040.0670.0600.0490.0870.0750.0690.0000.1500.2970.2010.1960.0000.455
curricular_units_2nd_sem_(without_evaluations)-0.0300.0940.064-0.0210.153-0.073-0.0470.3840.082-0.0270.159-0.064-0.0521.000-0.053-0.027-0.1110.0440.0370.0820.0000.0950.0000.0990.1470.0000.0000.0360.0000.0630.0710.0550.0400.0000.066
unemployment_rate-0.1040.0190.0240.1080.0670.0740.045-0.0670.0120.1390.0610.0700.042-0.0531.000-0.055-0.2880.0440.1760.1230.0930.1530.0410.1560.1350.1760.1740.1380.0480.1350.0910.0760.1300.0690.053
inflation_rate-0.0110.018-0.0020.017-0.040-0.002-0.037-0.068-0.0010.009-0.024-0.015-0.043-0.027-0.0551.000-0.1020.0400.1490.0900.0730.1270.0170.1220.1090.1290.1270.0580.0420.0860.0860.0680.0980.0300.036
gdp0.030-0.0570.0220.018-0.0970.0590.092-0.1840.0240.019-0.0040.0480.105-0.111-0.288-0.1021.0000.0380.1620.1200.0930.1390.0410.1410.1210.1630.1610.1400.0570.1380.0790.0810.1290.0650.052
marital_status0.0700.3130.0500.0670.0320.0510.0390.0550.0470.0340.0150.0480.0330.0440.0440.0400.0381.0000.2240.1670.3660.1550.0000.1340.0980.0570.0000.2750.0000.0300.0930.0440.1040.0000.078
application_mode0.1610.2880.2670.1980.1430.1870.1110.0580.2320.2000.1280.2890.1100.0370.1760.1490.1620.2241.0000.1680.4100.4120.2340.0880.0940.1060.0770.4110.0000.1640.2020.1770.2200.5300.221
course0.1430.1890.1170.4340.2410.2360.2710.1010.1160.4370.2450.2880.2280.0820.1230.0900.1200.1670.1681.0000.9980.1110.0400.0960.0870.0890.0890.3240.0620.1520.1390.4240.2220.0930.244
daytime/evening_attendance0.1850.4890.1710.2190.0630.1730.1380.0190.1820.1870.1080.1010.0850.0000.0930.0730.0930.3660.4100.9981.0000.1950.0000.2740.2400.1720.1260.2510.0230.0000.0350.0000.0920.0210.078
previous_qualification0.0860.1950.1830.1360.1030.1280.0930.0760.1760.1080.1140.1180.1040.0950.1530.1270.1390.1550.4120.1110.1951.0000.0000.0910.0980.0630.0500.2210.0000.1440.1540.1240.1390.0000.146
nationality0.0000.0000.0000.0000.1690.0000.0260.1680.0000.0000.0800.0000.0670.0000.0410.0170.0410.0000.2340.0400.0000.0001.0000.0000.0510.2150.1160.0290.0000.0900.0490.0290.0310.9980.026
mother's_qualification0.0330.1490.0870.0820.0320.0980.0650.0650.1210.1220.0040.0920.0600.0990.1560.1220.1410.1340.0880.0960.2740.0910.0001.0000.4310.2490.1890.1440.0230.0350.0560.0830.1820.1010.135
father's_qualification0.0380.1330.0710.0490.0770.0420.0520.0970.0340.0370.0000.0350.0490.1470.1350.1090.1210.0980.0940.0870.2400.0980.0510.4311.0000.1540.1820.1150.0000.0000.0970.0950.1700.0770.134
mother's_occupation0.0000.0840.0380.0340.0000.0560.0570.0520.0270.0370.0000.0580.0870.0000.1760.1290.1630.0570.1060.0890.1720.0630.2150.2490.1541.0000.5710.1020.0670.1170.0950.0380.1950.1720.161
father's_occupation0.0000.0620.0230.0030.0000.0000.0530.0000.0000.0570.0000.0240.0750.0000.1740.1270.1610.0000.0770.0890.1260.0500.1160.1890.1820.5711.0000.1120.0000.1380.0190.0890.2250.0600.140
displaced0.3820.3890.1070.1500.0980.1290.0870.0000.1280.1380.0680.1160.0690.0360.1380.0580.1400.2750.4110.3240.2510.2210.0290.1440.1150.1020.1121.0000.0000.0880.0940.1240.0710.0000.112
educational_special_needs0.0000.0000.0000.0430.0000.0000.0000.0000.0000.0000.0290.0000.0000.0000.0480.0420.0570.0000.0000.0620.0230.0000.0000.0230.0000.0670.0000.0001.0000.0000.0000.0030.0110.0000.000
debtor0.0900.1330.0570.0470.0610.1450.1060.0310.0540.0750.0590.1800.1500.0630.1350.0860.1380.0300.1640.1520.0000.1440.0900.0350.0000.1170.1380.0880.0001.0000.4070.0510.0650.0720.241
tuition_fees_up_to_date0.0730.2090.0000.0830.1030.2800.2480.0780.0000.1240.1240.3130.2970.0710.0910.0860.0790.0930.2020.1390.0350.1540.0490.0560.0970.0950.0190.0940.0000.4071.0000.1020.1360.0390.431
gender0.1080.1620.0270.2130.0790.2370.1930.0000.0280.1620.1090.2620.2010.0550.0760.0680.0810.0440.1770.4240.0000.1240.0290.0830.0950.0380.0890.1240.0030.0510.1021.0000.1680.0200.229
scholarship_holder0.0890.2120.0860.1560.1870.2640.1810.0570.0750.1130.1660.2730.1960.0400.1300.0980.1290.1040.2200.2220.0920.1390.0310.1820.1700.1950.2250.0710.0110.0650.1360.1681.0000.0220.304
international0.0000.0000.0000.0000.0560.0000.0000.0570.0000.0300.0110.0250.0000.0000.0690.0300.0650.0000.5300.0930.0210.0000.9980.1010.0770.1720.0600.0000.0000.0720.0390.0200.0221.0000.000
target0.0790.2190.0450.1730.2580.4550.3830.0620.0420.1390.2740.5160.4550.0660.0530.0360.0520.0780.2210.2440.0780.1460.0260.1350.1340.1610.1400.1120.0000.2410.4310.2290.3040.0001.000

Missing values

2023-01-11T16:47:23.345948image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-11T16:47:24.178812image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

marital_statusapplication_modeapplication_ordercoursedaytime/evening_attendanceprevious_qualificationnationalitymother's_qualificationfather's_qualificationmother's_occupationfather's_occupationdisplacededucational_special_needsdebtortuition_fees_up_to_dategenderscholarship_holderage_at_enrollmentinternationalcurricular_units_1st_sem_(credited)curricular_units_1st_sem_(enrolled)curricular_units_1st_sem_(evaluations)curricular_units_1st_sem_(approved)curricular_units_1st_sem_(grade)curricular_units_1st_sem_(without_evaluations)curricular_units_2nd_sem_(credited)curricular_units_2nd_sem_(enrolled)curricular_units_2nd_sem_(evaluations)curricular_units_2nd_sem_(approved)curricular_units_2nd_sem_(grade)curricular_units_2nd_sem_(without_evaluations)unemployment_rateinflation_rategdptarget
0Single2nd phase - general contingent5Animation and Multimedia DesigndaytimeSecondary educationPortugueseGeneral commerce courseOther - 11th Year of SchoolingPersonal Services, Security and Safety Workers and SellersUnskilled Workersyesnonoyesmaleno20no00000.000000000000.000000010.81.41.74Dropout
1SingleInternational student (bachelor)1TourismdaytimeSecondary educationPortugueseSecondary Education - 12th Year of Schooling or Eq.Higher Education - DegreeIntermediate Level Technicians and ProfessionsIntermediate Level Technicians and Professionsyesnononomaleno19no066614.0000000066613.666667013.9-0.30.79Graduate
2Single1st phase - general contingent5Communication DesigndaytimeSecondary educationPortugueseGeneral Course of Administration and CommerceBasic education 1st cycle (4th/5th year)Unskilled WorkersUnskilled Workersyesnononomaleno19no06000.000000006000.000000010.81.41.74Dropout
3Single2nd phase - general contingent2Journalism and CommunicationdaytimeSecondary educationPortugueseSupplementary Accounting and AdministrationBasic education 1st cycle (4th/5th year)Personal Services, Security and Safety Workers and SellersIntermediate Level Technicians and Professionsyesnonoyesfemaleno20no068613.42857100610512.40000009.4-0.8-3.12Graduate
4MarriedOver 23 years old1Social Service (evening attendance)eveningSecondary educationPortugueseGeneral Course of Administration and CommerceBasic Education 2nd Cycle (6th/7th/8th Year)Unskilled WorkersUnskilled Workersnononoyesfemaleno45no069512.3333330066613.000000013.9-0.30.79Graduate
5MarriedOver 23 years old1Management (evening attendance)eveningBasic education 3rd cycle (9th/10th/11th year)PortugueseGeneral Course of Administration and CommerceBasic education 1st cycle (4th/5th year)Unskilled WorkersSkilled Workers in Industry, Construction and Craftsmennonoyesyesmaleno50no0510511.85714300517511.500000516.20.3-0.92Graduate
6Single1st phase - general contingent1NursingdaytimeSecondary educationPortugueseGeneral commerce courseBasic Education 2nd Cycle (6th/7th/8th Year)Skilled Workers in Industry, Construction and CraftsmenArmed Forces Professionsyesnonoyesfemaleyes18no079713.3000000088814.345000015.52.8-4.06Graduate
7Single3rd phase - general contingent4TourismdaytimeSecondary educationPortugueseGeneral Course of Administration and CommerceBasic education 1st cycle (4th/5th year)Unskilled WorkersUnskilled Workersyesnononomaleno22no05500.000000005500.000000015.52.8-4.06Dropout
8Single1st phase - general contingent3Social ServicedaytimeSecondary educationRomanianSecondary Education - 12th Year of Schooling or Eq.Secondary Education - 12th Year of Schooling or Eq.Unskilled WorkersUnskilled Workersnononoyesfemaleyes21yes068613.8750000067614.142857016.20.3-0.92Graduate
9Single1st phase - general contingent1Social ServicedaytimeSecondary educationPortugueseSecondary Education - 12th Year of Schooling or Eq.Basic Education 3rd Cycle (9th/10th/11th Year)Administrative staffSkilled Workers in Industry, Construction and Craftsmenyesnoyesnofemaleno18no069511.40000000614213.50000008.91.43.51Dropout
marital_statusapplication_modeapplication_ordercoursedaytime/evening_attendanceprevious_qualificationnationalitymother's_qualificationfather's_qualificationmother's_occupationfather's_occupationdisplacededucational_special_needsdebtortuition_fees_up_to_dategenderscholarship_holderage_at_enrollmentinternationalcurricular_units_1st_sem_(credited)curricular_units_1st_sem_(enrolled)curricular_units_1st_sem_(evaluations)curricular_units_1st_sem_(approved)curricular_units_1st_sem_(grade)curricular_units_1st_sem_(without_evaluations)curricular_units_2nd_sem_(credited)curricular_units_2nd_sem_(enrolled)curricular_units_2nd_sem_(evaluations)curricular_units_2nd_sem_(approved)curricular_units_2nd_sem_(grade)curricular_units_2nd_sem_(without_evaluations)unemployment_rateinflation_rategdptarget
4414Single1st phase - general contingent1EquiniculturedaytimeSecondary educationPortugueseHigher Education - DegreeBasic Education 2nd Cycle (6th/7th/8th Year)Intermediate Level Technicians and ProfessionsPersonal Services, Security and Safety Workers and Sellersyesnonoyesfemaleno18no056511.8000001058511.60000009.4-0.8-3.12Graduate
4415DivorcedOver 23 years old1NursingdaytimeBasic education 3rd cycle (9th/10th/11th year)PortugueseGeneral Course of Administration and CommerceBasic education 1st cycle (4th/5th year)Farmers and Skilled Workers in Agriculture, Fisheries and ForestryFarmers and Skilled Workers in Agriculture, Fisheries and Forestrynonoyesnofemaleno46no0714312.33333300712311.083333011.10.62.02Dropout
4416SingleChange of course2NursingdaytimeSecondary educationPortugueseSupplementary Accounting and AdministrationBasic Education 2nd Cycle (6th/7th/8th Year)Unskilled WorkersPersonal Services, Security and Safety Workers and Sellersnononoyesfemaleno23no1114151212.62500011114151212.62500017.62.60.32Graduate
4417Single1st phase - general contingent1Communication DesigndaytimeSecondary educationPortugueseSecondary Education - 12th Year of Schooling or Eq.Secondary Education - 12th Year of Schooling or Eq.Unskilled WorkersUnskilled Workersyesnonoyesfemaleyes20no066613.8333330066613.500000016.20.3-0.92Graduate
4418SingleTechnological specialization diploma holders1Communication DesigndaytimeTechnological specialization coursePortugueseHigher Education - DegreeBasic Education 2nd Cycle (6th/7th/8th Year)Intermediate Level Technicians and ProfessionsUnskilled Workersnononoyesmaleno20no277612.50000005910713.142857116.20.3-0.92Graduate
4419Single1st phase - general contingent6Journalism and CommunicationdaytimeSecondary educationPortugueseSecondary Education - 12th Year of Schooling or Eq.Secondary Education - 12th Year of Schooling or Eq.Personal Services, Security and Safety Workers and SellersAdministrative staffnononoyesmaleno19no067513.6000000068512.666667015.52.8-4.06Graduate
4420Single1st phase - general contingent2Journalism and CommunicationdaytimeSecondary educationRussianSecondary Education - 12th Year of Schooling or Eq.Secondary Education - 12th Year of Schooling or Eq.Unskilled WorkersUnskilled Workersyesnoyesnofemaleno18yes066612.0000000066211.000000011.10.62.02Dropout
4421Single1st phase - general contingent1NursingdaytimeSecondary educationPortugueseGeneral Course of Administration and CommerceBasic education 1st cycle (4th/5th year)Unskilled WorkersUnskilled Workersyesnonoyesfemaleyes30no078714.9125000089113.500000013.9-0.30.79Dropout
4422Single1st phase - general contingent1ManagementdaytimeSecondary educationPortugueseGeneral Course of Administration and CommerceBasic education 1st cycle (4th/5th year)Skilled Workers in Industry, Construction and CraftsmenAdministrative staffyesnonoyesfemaleyes20no055513.8000000056512.00000009.4-0.8-3.12Graduate
4423SingleOrdinance No. 854-B/991Journalism and CommunicationdaytimeSecondary educationCape VerdeanSupplementary Accounting and AdministrationBasic education 1st cycle (4th/5th year)Personal Services, Security and Safety Workers and SellersUnskilled Workersyesnonoyesfemaleno22yes068611.6666670066613.000000012.73.7-1.70Graduate